Scholarlink

This research applies the Design Science Research (DSR) methodology to investigate how self-referencing in Large Language Model (LLM)-based health coaching influences user trust and perceptions of anthropomorphism. We synthesized theory-driven design principles to guide the integration of self-referencing and demonstrated them in a vignette-based prototype. Through a single-factorial between-subjects experiment, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative feedback, we identified a dual effect of self-referencing: while professional self-referencing enhances trust via increased anthropomorphism, overly personal references can directly undermine trust. Based on these findings, we refined our design principles to optimize trust-building in LLM-based coaching. Our contributions provide actionable design guidelines for creating more effective and trustworthy AI-driven health interventions, advancing the understanding of anthropomorphic design in digital coaching contexts.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Designing for Trust: Integrating Self-referencing in Large Language Model-Based Health Coaching

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sophia Meywirth,
  • Andreas Janson,
  • Matthias Söllner

摘要

This research applies the Design Science Research (DSR) methodology to investigate how self-referencing in Large Language Model (LLM)-based health coaching influences user trust and perceptions of anthropomorphism. We synthesized theory-driven design principles to guide the integration of self-referencing and demonstrated them in a vignette-based prototype. Through a single-factorial between-subjects experiment, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative feedback, we identified a dual effect of self-referencing: while professional self-referencing enhances trust via increased anthropomorphism, overly personal references can directly undermine trust. Based on these findings, we refined our design principles to optimize trust-building in LLM-based coaching. Our contributions provide actionable design guidelines for creating more effective and trustworthy AI-driven health interventions, advancing the understanding of anthropomorphic design in digital coaching contexts.